Top 10 Applications of Computer Vision, use case and examples

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Computer vision is the field of artificial intelligence that allows us to teach people how to do things. Computer vision helps computers and systems provide meaningful information from digital images and video with the help of machine learning and neural networks.

Computer vision allows to computers to analyze the visual world and understand environments. It applies to machine learning to classify objects in digital images and videos and allow computers to see what they see. There are different types of computer vision, like image detection, face recognition, edge detection, and image classification. Artificial intelligence allows computers to think, while computer vision allows them to see, observe, and understand.

Computer vision works much like human vision, but humans have further vision. Human vision has the benefit of a lifetime of context to learn how to distinguish between objects, how far away they are, whether they are moving, and whether there is something wrong with the image.

Computer vision trains machines to perform these tasks, but it must do so in a much shorter time, using cameras, data, and algorithms instead of the retina, optic nerve, and visual cortex. Systems trained to inspect products and monitor production assets can rapidly surpass human capabilities by analyzing thousands of products and processes every minute to discover invisible defects and problems.

Human Vision and computer vision

Humans observe our surroundings with the help of the retina and optical nerve systems. Measure the differences and speed, and analyze the mistakes. Similarly, computer vision allows us to perform these tasks with the help of artificial intelligence. Computer vision uses a combination of algorithms and data.

But as humans, machines did not get tired; trained machines analyzed the number of productions and assets with computer vision. It detects small-to-small defects, but the human eye cannot detect them.

What is computer vision?

Computer vision uses machine learning and neural networks to help computers and systems obtain meaningful information from digital images, video, and other visual inputs, make recommendations, and take action when faults or problems are identified. This is the field of artificial intelligence (AI) that teaches people how to do things.

While AI allows computers to think, computer vision allows them to see, observe, and understand.

How does computer vision work?

Computer vision requires large amounts of data. It analyzes the data repeatedly until it identifies differences and eventually recognizes the image. For example, training a computer to recognize car tires requires inputting large amounts of tire images and tire-related objects to learn the difference and recognize tires, especially tires that are free from faults.

To achieve this, two important technologies are used: a type of machine learning called deep learning and convolutional neural networks (CNN).

Machine learning uses algorithmic models that allow computers to learn about the context of visual data. Once enough data is fed into the model, the computer “looks” at that data and learns to distinguish one image from another. Algorithms allow machines to learn on their own rather than being programmed to recognize images.

CNNs help machine learning or deep learning models “see” images by dividing them into pixels given tags or labels. Use the labels to perform convolution (a mathematical operation of two functions to produce a third function) to make predictions about what you are “looking at.” The neural network performs convolution and checks the accuracy of the predictions in a series of iterations until the predictions become reality. Then, it recognizes and sees images just like humans do.

Similar to how humans view images from a distance, a CNN first identifies hard edges and simple shapes, then feeds the information as it iteratively makes predictions. CNN is used to understand a single image. Recurrent neural networks (RNNs) are used in a similar way in video applications to help computers understand how images in a series of frames are related to each other.

Top 10 Applications of Computer vision

There is a lot of research being done in the field of computer vision, but it is not the only one. Real-world applications demonstrate how important computer vision is in business, entertainment, transportation, health care, and everyday life endeavors. A major factor driving the growth of these applications is the large amount of visual information coming from smartphones, security systems, traffic cameras, and other visual devices. This data can play an important role in the operations of various industries, but it is not used today. This information forms a testing ground for training computer vision applications and a starting point for becoming part of various human activities.

IBM used computer vision to create My Moments for the 2018 Masters golf tournament. IBM Watson® looked at hundreds of hours of master footage and was able to recognize the sights (and sounds) of key shots. We have picked these important moments and brought them to our fans in the form of personalized highlight reels.
Google Translate allows users to point their smartphone camera at a sign in another language and get a translation of the sign into their preferred language almost instantly.
The development of self-driving cars relies on computer vision to understand visual input from the car’s cameras and other sensors. It is essential to identify other cars, traffic signs, lane markers, pedestrians, cyclists, and all other visual information you encounter on the road.
IBM is applying computer vision technology with partners like Verizon to bring intelligent AI to the edge and help automakers identify quality defects before they leave the factory.

The human eye is incredibly capable, but modern computer vision is working hard to catch up. Below are the top 10 applications for computer vision in 2022.

1. Agriculture

Agriculture has traditionally not been associated with cutting-edge technology. However, outdated methods and equipment are gradually being removed from agricultural fields around the world. Farmers are now taking advantage of computer vision to improve agricultural productivity.

Companies specializing in agricultural technology develop advanced computer vision and artificial intelligence models for seeding and harvesting purposes. These solutions also help with weed control, plant health detection, and advanced weather analysis.

Computer vision has many existing and upcoming applications in agriculture, including drone-based crop monitoring, automated pesticide spraying, yield tracking, and smart crop sorting and classification. These AI-powered solutions scan and analyze the shape, color, and texture of crops. Weather records, forest data, and on-site security are also increasingly being used through computer vision technology.

2. self-Driving Car

2022 is the year of self-driving cars. Market leaders like Tesla are making great strides thanks to advanced technologies like computer vision and 5G.

Tesla’s self-driving cars use multi-camera setups to analyze their surroundings. This allows the vehicle to provide users with advanced features such as autopilot. The vehicle also uses a 360-degree camera to detect and classify objects through computer vision.

Drivers of self-driving cars can either drive manually or let the vehicle make autonomous decisions. If users choose the latter configuration, these vehicles use computer vision to engage in advanced processes such as route planning, driving scene recognition, and speed arbitration.

3. Facial Recognition

While facial recognition is already used at the individual level, such as through smartphone applications, the public safety industry is also a notable driver of facial recognition solutions. Detecting and recognizing faces in public places is a controversial application of computer vision that has already been introduced in some jurisdictions and banned in others.

Successful facial recognition requires deep learning and machine vision. Computer vision algorithms detect and capture images of people’s faces in public places. This data is sent to the backend system for analysis. Common facial recognition solutions for large-scale public use combine analysis and recognition algorithms.

Advocates favor facial recognition using computer vision because it can help detect and prevent criminal activity. These solutions also have applications for tracking specific individuals for security missions.

4. Human Posture Tracking

Human pose tracking models use computer vision to process visual input and estimate human pose. Human pose tracking is another feature of computer vision that is used in industries like gaming, robotics, fitness apps, and physical therapy.

For example, the Microsoft Kinect gaming device can use AI vision to accurately monitor player movements. It works by detecting the positions of the joints of the human skeleton on a 3D plane and recognizing their movements.

5. Interactive entertainment

Gone are the days when digital entertainment meant audiences just sat and watched without participating. Today, interactive entertainment solutions leverage computer vision to deliver truly immersive experiences. Cutting-edge entertainment services use artificial intelligence to engage users in dynamic experiences.

For example, Google Glass and other smart eyewear demonstrate how users can receive information about what they are looking at. Information is transmitted directly to the user’s field of vision. These devices also respond to head movements and facial changes, allowing users to send commands simply by moving their heads.

6. Medical imaging

Medical systems rely heavily on pattern detection and image classification principles for diagnosis. Although these activities were primarily performed manually by qualified medical professionals, computer vision solutions are increasingly being used to assist doctors in diagnosing medical conditions.

The application of computer vision techniques to the processing of medical images is notable. This is particularly common in pathology, radiology, and ophthalmology. Visual pattern recognition through computer vision enables advanced products like Microsoft InnerEye to provide fast and accurate diagnostics in a growing number of medical specialties.

7. Manufacturing

Manufacturing is one of the most technology-intensive processes in the modern world. Computer vision is prevalent in manufacturing plants and is commonly used in AI-powered inspection systems. Such systems are popular in research and development laboratories and warehouses, allowing these facilities to operate more intelligently and efficiently.

For example, predictive maintenance systems use computer vision to inspect systems. These devices continuously scan the environment to reduce machine failures and product distortion. If a potential failure or poor-quality product is detected, the system notifies personnel so they can take further action. Additionally, computer vision is used by workers in packaging operations and quality monitoring activities.

Thanks to the advancements brought by Industry 4.0, computer vision is also being used to automate labor-intensive processes like product assembly and management. AI-powered product assembly is most commonly seen on assembly lines for precision goods such as electronics. Companies like Tesla have fully automated their factory manufacturing processes.

8. Retail Management

While interaction-free shopping experiences have always been the inevitable future, the COVID-19 pandemic has certainly helped accelerate the adoption of computer vision applications in the retail industry. Tech giants like Amazon are now actively exploring ways to use AI vision to revolutionize retail and enable “pick-up and drop-off” of customers.

Retail stores are already using computer vision solutions to monitor shopper activity, making loss prevention non-intrusive and customer-friendly. Computer vision is also used to analyze customer moods and personalize advertising. Additionally, AI-powered vision solutions are being used to maximize ROI through evaluating customer retention programs, inventory tracking, and product placement strategies.

9. Education

As the COVID-19 pandemic has brought distance learning into the spotlight, the education technology industry is also leveraging computer vision for a variety of applications. For example, teachers use computer vision solutions for non-intrusive assessment of the learning process. These solutions allow teachers to identify missing students and improve the instructional process to ensure no student is left behind.

Furthermore, AI vision is also used in applications such as school logistics, knowledge acquisition, attendance monitoring, and periodic evaluation. A common example of this is computer vision-enabled webcams used to monitor students during exams. This makes it easier to detect fraudulent activity by analyzing eye movements and body movements.

10. Transportation

Finally, computer vision systems are increasingly being applied to improve transportation efficiency. For example, computer vision is being used to detect traffic light violators, allowing law enforcement to reduce dangerous behavior on the road.

Intelligent sensing and processing solutions are also used to detect speeding, erratic driving, and other disturbances. Additionally, computer vision is used in intelligent transportation systems for traffic flow analysis.

Examples of Computer vision

Many organizations do not have the resources to fund computer vision labs or build deep learning models and neural networks. You may also lack the computing power needed to process large sets of visual data. Companies like IBM are helping by providing computer vision software development services. These services provide pre-built learning models available from the cloud and also reduce the demand on computing resources. Users connect to services through application programming interfaces (APIs) and use them to develop computer vision applications.

IBM has also introduced a computer vision platform that addresses both development and computing resource concerns. IBM Maximo® Visual Inspection includes tools that enable subject matter experts to label, train, and deploy deep learning vision models without any coding or deep learning expertise. Vision models can be deployed in local data centers, in the cloud, and on edge devices.

Although it is becoming easier to obtain resources to develop computer vision applications, an important question that must be answered quickly is: “What do these applications actually do?” Understanding and defining specific computer vision tasks makes it easier to focus, validate, and initiate projects and applications.

Here are some examples of established computer vision tasks.

Image classification allows you to classify images by sight (dog, apple, human face). More precisely, it can accurately predict whether a particular image belongs to a particular class. For example, social media companies may want to use it to automatically identify and isolate offensive images uploaded by users.
Object detection uses image classification to identify specific classes of images and allows you to detect and tabulate their presence within an image or video. Examples include detecting damage on an assembly line or identifying machines that require maintenance.
Object tracking Tracks or follows an object once it has been detected. This work is often done using images captured in sequences or real-time video feeds. For example, self-driving cars need to classify and detect not only objects such as pedestrians, other cars, and road infrastructure but also moving objects to avoid collisions and comply with traffic laws. Also needs to be tracked. 7
Content-based image search uses computer vision to browse, search, and retrieve images from large data stores based on the content of the image rather than the metadata tags associated with the image. This work may involve automatic image annotation to replace manual image tagging. These functions can be used in digital asset management systems to improve search and retrieval accuracy.

1. Google Translate

In 2015, technology leader Google launched an instant translation service that leverages computer vision through your smartphone’s camera. Neural Machine Translation is a leading computer vision-based system for quick, accurate translation that was incorporated into Google Translate web results in 2016.

When you open the app on an Internet-enabled device with a camera, the camera detects real-world text. The app automatically detects the text and translates it into the user’s selected language. For example, if you point your camera at a sign or poster containing text in another language, you can read the text in your selected language on your smartphone screen.

In addition to translation, Google also uses computer vision in its Lens service. Both services can translate over 100 languages instantly. Google’s Translate service is already benefiting users in Asia, Africa, and Europe, where many languages are concentrated in relatively small areas.

Over the past few years, more than half of Google’s Translate Toolkit languages have become available offline. Therefore, these neural net-powered translations do not require a network connection.

2. Facebook 3D Photo

Tech giant Meta (formerly known as Facebook) is also trying its hand at computer vision for a variety of exciting applications. One such application is converting 2D images into 3D models.

Facebook 3D Photos, launched in 2018, initially required a smartphone with dual cameras to create 3D images and depth maps. Although the popularity of this feature was initially limited, the widespread adoption of affordably priced dual-camera cell phones powered by computer vision has increased the use of this feature.

3D Photo converts regular 2D photos into 3D images. Users can rotate, tilt, or scroll their smartphones to view these photos from different perspectives. Machine learning is used to extrapolate the 3D shape of objects depicted in images. This process applies realistic 3D effects to your images.

Advances in computer vision algorithms used by Meta have made it possible to apply 3D photography features to any image. The feature has become popular among Facebook users, as you can now turn decades-old photos into 3D using a mid-range Android or iOS smartphone.

Meta is not alone in exploring the application of computer vision in 2D to 3D image conversion. Google-backed DeepMind and GPU market leader Nvidia are both experimenting with AI systems that allow computers to view images from different angles, just like humans do.

3. yolo

YOLO stands for You Only Look Once and is a pre-trained object detection model powered by transfer learning. It can be used for a variety of purposes, including enforcing social distancing guidelines.

As a computer vision solution, YOLO algorithms can detect and identify objects in visual input in real time. This is achieved by using a convolutional neural network that can simultaneously predict different bounding boxes and class probabilities.

As the name suggests, YOLO can detect objects by passing an image through the neural network only once. This algorithm accomplishes the prediction of the entire image within a single algorithm run. It can “learn” new things quickly and effectively, store data about object representations, and take advantage of this information to locate objects.

Enforcing social distancing measures during the height of the COVID-19 pandemic was critical but extremely difficult for jurisdictions with limited resources and large populations. To address this problem, authorities in some parts of the world have adopted computer vision solutions like YOLO to develop social distancing tools.

YOLO can track people in a specific geographical area and determine whether social distancing norms are being followed or not. Apply object detection and tracking principles in real-time to detect social distancing violations and alert relevant authorities.

In practice, YOLO works by using bounding boxes to capture each individual in the visual input. The movement of these boxes is tracked within the frame, and the distance between them is constantly recalculated. If violations of social distancing guidelines are detected, the algorithm highlights the bounding boxes of the violators so that further action can be initiated.

4. Face App

FaceApp is a popular image manipulation application that alters the visual input of a human face to change gender, age, and other characteristics. This is accomplished through Deep Convolutional Generative Adversarial Networks, a specific subtype of computer vision.

FaceApp combines image recognition principles and deep learning to recognize key facial features, such as cheekbones, eyelids, the bridge of the nose, and the jaw line. Once these features outline the human face, the app can modify them to transform the image.

FaceApp works by collecting sample data from multiple users’ smartphones and feeding it into a deep neural network. This allows the system to “learn” every detail of the human facial appearance. These learnings are used to enhance the app’s predictive capabilities, allowing it to simulate wrinkles, touch up hair lines, and make other realistic changes to images of human faces. Become.

FaceApp relies on computer vision to recognize patterns. Its artificial intelligence capabilities allow it to use data obtained from multiple sources to mimic images with increasing efficiency over time. FaceApp transfers facial information from one photo to another at the micro level. It offers great functionality at the macro level, allowing the app to process photos from millions of users and build a large database.

5. Sentio Scope

Sentio Scope is a fitness and sports tracking system developed by Sentio. It primarily serves as a soccer player tracking solution, processing real-time visual input from live games. The recorded data is uploaded to a cloud-based analysis platform.

Centiscope relies on a 4K camera setup to capture visual input. It then processes these inputs to locate players and get real-time information from their activities and actions.

This computer vision-powered solution creates a conceptual model of a soccer field and represents the game in a two-dimensional world. This 2D model is divided into a grid of dense spatial cells. Each cell represents a unique ground point on the field and is shown as a fixed image patch in the video.

Sentioscope is powered by machine learning and trained on over 100,000 player samples. This makes it possible to detect “player” cells from soccer match footage. Probabilistic algorithms work in a variety of difficult visibility conditions.

Sentio is one of many companies working to bring computer vision to sports training. These solutions typically analyze live feeds from high-resolution cameras to track moving balls, locate player positions, and provide other useful information that can be used to improve player and team performance.

Frequently Asked Question

Why computer vision?

Computer vision enables a wide range of innovations. This allows self-driving cars to operate safely on roads and highways. Facial recognition tools will be able to match images of people’s faces to their identities. It also allows augmented reality applications to mix virtual objects and real-world images.

Computer vision applications are used in a variety of industries to improve the consumer experience, reduce costs, and increase security. Manufacturers use it to detect defective products on the assembly line and prevent them from being shipped to customers. Insurance adjusters use it to assess vehicle damage and reduce fraud in the insurance claims process. Medical professionals use it to scan X-rays, MRIs, and ultrasounds to detect health problems. Banks use it to verify a customer’s identity before making large transactions.

What is meant by computer vision in artificial intelligence?

Computer vision is a field of computer science that focuses on enabling computers to recognize and understand objects and people in images and videos. Like other types of AI, computer vision aims to perform and automate tasks that replicate human capabilities.

What is computer vision, for example?

It analyzes the data repeatedly until it identifies differences and eventually recognizes the image. For example, training a computer to recognize car tires requires inputting large amounts of tire images and tire-related objects to learn the difference and recognize tires, especially tires that are free from faults.

What are the benefits of computer vision?

Automation: Computer vision can automate a wide range of tasks that traditionally require human intervention, such as quality control, inventory management, and safety monitoring in manufacturing.

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